AI Project Life Cycle: Key Stages of Product Development Success

In traditional software development, you are probably used to a pretty straightforward process. Plan, code, test, deploy, and you’re done. But this approach is not the same with AI. There are extra steps, unique challenges, and a lot more iteration involved. What happens when businesses neglect these differences? They end up with models that work incorrectly, unforeseen expenses and delays.  

As a software development company passionate about AI and new technologies, we at Flyaps have helped many businesses from different domains to achieve their goals with AI development. Whether it's custom AI solutions or pre-built AI tools for industries like logistics, retail, and recruitment, we've been there, experimenting and creating unique AI-driven solutions. Having acquired a lot of unique knowledge, we would like to share our insights about all stages of the AI project lifecycle and give you one real-life example. Keep on reading to learn more!

What makes AI projects unique?

So why do AI projects need this special treatment? Sure thing, we’re talking about developing brand new types of solutions, powered by generative AI in many cases. So unlike the traditional software development approach that’s been around for ages, AI projects are different and companies have to deal with unique challenges they haven’t encountered before. With that said, let's discuss some AI project specifics.

Data is EVERYTHING

Before you even start thinking about your AI project-to-be, you have to consider the data you will have to prepare. How much data will your AI solution need? What sources of data to choose from? Should data be high quality or not?

Let’s say you're building an AI system to predict whether a customer will buy a product based on their browsing behavior on an e-commerce site. You don't need mountains of data – just the right kind. If you only have data from a handful of customers who have bought something, your AI won't have much to learn from. But if you have a good mix of data from both buyers and prospects, your AI can start to spot patterns and make accurate predictions. So, while it's not about having tons of data in this case, having good quality data is key here to making your AI system perform well.

But in some cases, you will prioritize quantity over quality. For example, if you're training an AI model to recognize handwritten digits for automatic postcode sorting. In this case, a large number of different handwriting samples is crucial for the model to learn and operate as intended.

Anyway, it all comes down to data. It’s a base on which the AI models work. Think of it as a person's brain that learns and makes decisions based on experience. The more relevant the experiences (or data), the better the brain (or AI) can understand and predict.

Training AI models takes time

Instead of just getting a program to do a certain task, you have to actually train an AI model, which takes time and patience. We'll talk about the training phase in more detail later, but for now, it's important to remember that training a model is a lot like teaching a child.

Imagine a child sitting down at a piano for the first time. At first, they may not know where to start or how to make sense of the keys. Similarly, when you train an AI model, you're essentially providing it with the “theory” or basic knowledge it needs to understand the task at hand. It's called prompt engineering, when developers create a clear prompt for the model explaining the desired output.

Once the child understands the basics of piano playing - the notes, the scales, the finger positions - it's time for repetition.

Just like practicing scales or exercises, training an AI model involves going through many examples over and over again, getting better with each iteration.

Now that we’ve discussed specific characteristics of AI projects, let’s take a closer look at the main stages of the AI development cycle, and what it looks like today.

The key stages of the AI project cycle

As AI is getting more and more powerful, with the scope of work it can perform expanding every day, the AI project life cycle is becoming more complex as well. Below we will share only the most essential stages no AI project can live without, but their exact number depends on your project needs.

Problem identification

Before opting for AI, you have to decide whether AI is a must-have in your case. While generative AI and all these new solutions are indeed remarkable, there are still solutions out there that can replace AI-powered ones with ease.

For instance, if we’re talking about a simple function that can be performed by other automation tools, then go for it. It will be cheaper, faster and may require fewer computing resources than AI. Here’s an example for better understanding.

Let's say your company processes hundreds of invoices each month and you've set up an AI-based invoice processing system to automate the process. The system can handle different invoice formats and adapt to new ones over time. However, the solution requires a lot of investment in computing resources and continuous maintenance.

On the other hand, your competitor chose to use RPA for their invoice processing. Since their clients' invoices were all pretty similar, RPA was able to automate the process really efficiently and at a fraction of the cost. The RPA system needs less maintenance and is quicker to deploy.

So you get it. If your challenge is niche and automation tools won’t be able to help, you have no choice but to adapt AI algorithms. If the issue is more straightforward, look for simpler solutions.

Data preparation

Data preparation is a long story, filled with many nuances. We will cover the main points here, and if you’re looking to find out how to create a strong data foundation for AI, our other article is waiting for you.

First of all, you need to decide what data and what amount of it you need for your project. Let's say an airline decides to add a chatbot to its app to provide information about current flight delays, taking into account weather conditions for predicting the likelihood of delays and offering rebooking options.

To do this, the airline needs to collect several types of data. First, they collect historical and real-time flight status data, including information on departure and arrival times, delays and cancellations. Next, they integrate weather forecasts and historical weather data for all destinations and major hubs, as weather conditions are a significant factor in flight delays. In addition, they take into account passenger data such as booking details, frequent flyer status and travel history, ensuring that all data collection complies with data protection regulations.

Once the data sources have been identified, the airline’s development team then cleanses and pre-processes the data. This involves removing duplicates, correcting errors and handling missing values to ensure the data quality. Data formats are standardized to ensure consistency across different sources, which is essential for accurate analysis. The cleaned and standardized data is then merged into a single dataset, ready for further analysis.

One important tool for managing data for AI projects is a data pipeline. It is a system that moves data from different sources to repositories, where it can be analyzed, labeled and used for AI tasks. In the case of the airline, the sources would be flight data APIs like FlightAware or FlightStats, as well as the airline's own database.

You can find more information in our article on how to build a data pipeline fast (for AI and LLM projects).

Feature engineering

Suppose an AI project requires a model to predict specific things, like which products will sell best next month. To achieve this, data engineers need to make changes in the algorithm. They start by selecting the most relevant pieces of raw data and then determine the measurable characteristics that the algorithm will use to predict the likelihood of these sales accurately. These measurable characteristics are called “features,” and the entire process of transforming raw data into useful features is known as feature engineering. This process helps ensure the model can make accurate and reliable predictions.

Let's take the bestselling products we've mentioned. In this case, data engineers start with raw data such as past sales, product reviews and customer demographics. Then they create new features, such as the average rating of each product, the number of times a product is purchased with other specific products and seasonal trends. They transform this data to ensure it's on the same scale, extract the most useful information and select the features that best predict future sales.

Model training

Once the data is ready, the AI model undergoes iterative training. Different machine learning algorithms and datasets are tested to find the best-performing model. For example, a retail company might test some algorithms and neural networks to predict customer churn. The optimal model is then fine-tuned by adjusting parameters such as learning rate, batch size, number of hidden layers, activation functions, and regularization to achieve the best possible results.

Additionally, businesses can use transfer learning to save time and resources. For instance, an e-commerce company could use a pre-trained image recognition model to categorize product images, eliminating the need to train a model from scratch.

Model evaluation

After training, the AI model's performance is evaluated using various metrics:

• Accuracy: the percentage of correct predictions.
• Logarithmic loss: a measure of how far the predictions are from the actual labels.
• Precision: the proportion of how many selected items are relevant.
• Recall: the proportion of how many relevant items are selected.
• F1 score: the accuracy of a test, taking into account a balance between precision and recall.
  

For instance, a retail company developing a model to predict customer churn would use these metrics to assess how well the model identifies customers who are likely to stop buying from them.

Model deployment

Once the AI model is set up, developers can start using it in a production environment to generate useful outputs. There are a few ways they can do this.

• Batch inference: generating predictions on a set schedule. For example, a bank might run credit risk assessments in batches every night.
• On-premises deployment: when AI-driven software is deployed on local hardware. For instance, a manufacturing company might use its own infrastructure to deploy a quality control model on its factory floor. The model checks products for defects as they come off the production line.
• Cloud deployment: when AI-driven software uses remote servers from providers like AWS or Google Cloud. This means that instead of running on local hardware, the software runs on remote servers. An online retailer, for instance, might deploy a recommendation system on the cloud. This system suggests products to customers while they shop online, and the cloud setup allows it to handle many users at once.
• Edge deployment: running models on local devices such as smartphones or IoT devices, rather than hardware and remote servers. For example, a smart thermostat could use a model deployed on the device to automatically change the temperature in the home based on user behavior (at the level and time that the user usually does).
  

Model monitoring

As data patterns change over time, AI models can degrade if they aren't monitored regularly. So, it's important to keep an eye on the models to make sure they're performing well. For example, a financial institution might use MLOps practices to continuously monitor a fraud detection model, ensuring it adapts to new types of fraudulent activities.

Model maintenance

Models need to be also maintained to ensure they stay reliable and accurate. One approach is to set up a model retraining pipeline that automatically updates the model with new data. For instance, an online news platform might retrain its content recommendation model weekly to account for changing reader preferences. Another approach is reinforcement learning, where the model improves based on feedback. A self-driving car, for example, can refine its navigation model based on real-world driving experiences.

Now, let’s get more practical. In the next section, we’ll show you how AI projects are usually implemented in real life, using our own case as an example.

A real-life example of an AI project life cycle

CV Compiler is an ML-based resume parser developed by Flyaps for Relocate.me – a platform that helps IT professionals find a job in any location around the world where they would like to work. Let’s overview this project step by step.

Problem definition

The Relocate.me team noticed that many candidates were getting rejected for jobs without because of poorly written resumes. The team wanted to create a tool that would improve the quality of tech resumes, making them more appealing to recruiters. AI was the perfect solution in this case, because it could quickly analyze and understand large volumes of text, spot patterns and common issues in resumes, and provide recommendations. This is much faster and more efficient than having humans review and suggest improvements for each resume.

Data acquisition and preparation

After discussing the project goals and objectives, we needed to collect some data, in this case – a lot of resumes. We used CVs from Relocate.me, as well as from other sources, especially in PDF format. Our team also added a built-in glossary of IT terms to help the future system understand and sort the info in these resumes.

Model development and training

Next, we used natural language processing (NLP) and created a custom ML model. This model was trained to spot key parts of a resume, like the summary, work experience, and tech skills, and give personalized tips for improvement.

Model evaluation and refinement

Throughout the project, we kept checking how well the model was performing. Our team used different techniques to make sure the text extraction was accurate and the suggestions were on point. We also did a lot of testing to ensure the system worked smoothly and made improvements based on the results.

Deployment

Next, Flyaps launched the system as a cloud-based app. This setup allowed different parts of the system to work independently, making it easier to update and scale. The backend handled the complex text parsing, while another company took care of the user-friendly frontend.

Machine learning operations (MLOps)

To keep the ML models up and running, we set up MLOps practices. This included automating workflows for model training and deployment, ensuring the system could quickly adapt to new data and user needs. Our team also built a low-code platform, allowing users to easily tweak and update the resume analysis features, making the system flexible for different businesses.

As a result, CV Compiler can analyze and improve resumes in less than a second. Moreover, other companies, like Indeed, can also integrate the solution into their systems to enhance their HR process. This flexibility has made CV Compiler a highly popular tool in the recruitment industry, with over 50,000 IT professionals using it.

Final thought

It's not enough to just know the basics of the AI project life cycle to succeed. AI projects are complex, especially when talking about generative AI projects that require more complex algorithms for training and careful evaluation, often with human judgment. It requires a lot of difficult choices, therefore experience in AI project management is crucial.

If you're looking for a partner to help you navigate an AI project, we at Flyaps are here for you. Drop us a line today to learn more about how we can assist you.